Improving the trustworthiness of image classification models by utilizing bounding-box annotations

Dharma KC; Chicheng Zhang

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arXiv · 2108.10131

Improving the trustworthiness of image classification models by utilizing bounding-box annotations

Dharma KC·Chicheng Zhang

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Abstract

We study utilizing auxiliary information in training data to improve the trustworthiness of machine learning models. Specifically, in the context of image classification, we propose to optimize a training objective that incorporates bounding box information, which is available in many image classification datasets. Preliminary experimental results show that the proposed algorithm achieves better performance in accuracy, robustness, and interpretability compared with baselines.

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Dharma KC, Chicheng Zhang. 2021-08-15. Improving the trustworthiness of image classification models by utilizing bounding-box annotations. https://arxiv.org/abs/2108.10131

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